Systems and methods for identifying solutions to computer problems using contexts and versions

ABSTRACT

A computer system includes a main system that executes an application in cooperation with a human user. An auxiliary system evaluates problems that occur in the main system. The auxiliary system includes a service module that collects problem related data from the main system, an acquisition module that acquires knowledge representations, a knowledge module that stores knowledge representations, an inference module that processes problem related data with knowledge representations to identify solutions and that forwards the solutions through the service module to the main system. The auxiliary system distinguishes a context of the problems and distinguishes versions of the main system.

This application is a national stage filing under 35 U.S.C. § 371 ofInternational Application No. PCT/EP2003/010246, filed Sep. 12, 2003,which published in the English language, which claims priority to EP02024530.4, filed Oct. 31, 2002.

FIELD OF INVENTION

The present invention generally relates to data processing by computersystems, programs, and methods. More particularly, the invention relatesto evaluating and solving problems. The computer systems aredistinguished into main, auxiliary and service systems.

BACKGROUND

Electronic data processing uses integrated and distributed computersystems with complex architecture. Coupling different computers overnetworks (e.g., Internet) enhances functionality but adds complexity andincreases maintenance.

Each computer system operates in the complexity of hardware (e.g.,computers and network) and software (e.g., operating systems,applications, databases).

Problems are deviations from the predefined operation of the computersystem that are caused by malfunction of hardware or software or byimproper input by the user. To name a few examples, components likeprocessors suddenly fail, applications occasionally provide wrongresults, and users sometimes manipulate data.

Problems often remain hidden from the user. Once detected, the userengages in problem solving. For example, the user reads documentationpapers, activates help functions (e.g., predefined advices, oftenobtained via online services), looks up in databases to identify advices(“notes”), makes experiments, or tells problem symptoms to specialists(e.g., through phone hotline, email, Internet portal).

A majority of users relies on passive assistance; only a minorityactively solves the problem. There are further challenges: For example,sensitive data remains with the authorized user but is shielded fromspecialists (data protection); users and specialists might introducefurther errors. In any case, problem solving remains time consuming andexpensive.

Further, heterogeneous system landscapes have systems that differ forexample, by manufacturer, release version, and application. Eachdifference increases the number of potential problems and correspondingsolutions. Selecting solutions becomes critical.

There is a need to improve problem solving by mitigating disadvantagesof the prior art.

BRIEF DESCRIPTION OF THE DRAWINGS

The accompanying drawings, which are incorporated in and constitute apart of this specification, illustrate embodiments of the invention andtogether with the description, serve to explain the principles of theinvention.

FIG. 1 illustrates a simplified block diagram of a computer system witha main system and an auxiliary system according to the presentinvention;

FIG. 2 illustrates the system of FIG. 1 with more detail;

FIG. 3 illustrates a service module in the auxiliary system with moredetail;

FIG. 4 illustrates an acquisition module in the auxiliary system withmore detail;

FIG. 5 illustrates a knowledge module in the auxiliary system with moredetail;

FIG. 6 illustrates an inference module in the auxiliary system with moredetail;

FIG. 7 illustrates a first distributed system landscape with the mainand auxiliary systems coupled to a service system;

FIG. 8 illustrates a second distributed system landscape with 2 mainsystems coupled to a service system;

FIG. 9 illustrates a simplified flowchart diagram of a method foroperating the main, auxiliary and service systems;

FIG. 10 illustrates a simplified scenario that considers interaction,and thereby distinguishes automatically problem evaluating andsemi-automatically problem evaluating;

FIG. 11 illustrates a simplified scenario that considers primary andsecondary context;

FIG. 12 illustrates a simplified scenario that considers thedistribution of problem collecting and solution processing in the systemlandscapes;

FIG. 13 illustrates further simplified scenarios;

FIG. 14 summarizes various aspects of the present invention byconcentrating on an inference module; and

FIG. 15 illustrates a simplified block diagram of a computer system ingeneral for that the present invention can be implemented.

DETAILED DESCRIPTION

An exemplary implementation for the invention uses the well-known systemR/3. R/3 is commercially available from SAP Aktiengesellschaft Walldorf(Baden, Germany, “SAP”). Organizations (i.e. SAP customers) useenterprise resource planning applications (“ERP applications”, or“business applications”) to organize information in a variety of fields,such as supply chain management (SCM), customer relationship management(CRM), financials, human resources (HR), enterprise portals, exchanges,technology, product lifecycle management (PLM), supplier relationshipmanagement (SRM), business intelligence, business intelligence, mobilebusiness, hosted solutions, small and midsize business, and industrysolutions.

ABAB/4 is the well-known programming language used by SAP to definetransactions for applications. R/3 and ABAB/4 is documented by a varietyof reference books, such as:

Gareth M. de Bruyn, Robert Lyfareff, Ken Kroes: “Advanced ABAPProgramming for SAP”. Prima Publishing 1999. ISBN 0-7615-1798-7.

Bernd Matzke: “Programming the SAP R/3 System”. Addison-Wesley, 1997.ISBN 0-201-92471-4.

Jonathan Blain, ASAP World Consultancy: “Special Edition Using SAP R/3,Third Edition. Que. 1999. ISBN 0-7897-1821-9.

A detailed description of a computer system in general and a list ofreference numbers are provided as the end of the specification.

In short, according to the invention, a computer system has a mainsystem to execute an application (A) in cooperation with a human userand has an auxiliary expert system to evaluate problems (P) in the mainsystem. Optionally, the problems are solved by predefined instructions.

The auxiliary system uses knowledge representations (R) in primary andsecondary contexts. The auxiliary system distinguishes versions of themain system and—optionally—versions of the application (A). Knowledgerepresentations (R) are classified into context groups.

The present invention enables the user to actively solve problems in themain system mostly without asking for advice by human specialists.Applying the invention saves time and quickly returns the main systemback to normal operation. Applying further features effectivelyescalates problem evaluation to the service system. Involving humanspecialists (often expensive) is only required as a last remedy.

FIG. 1 illustrates a simplified block diagram of a computer system withmain system 200 and auxiliary system 300 according to the presentinvention.

Computer system 200/300 has main system 200 to execute application A incooperation with human user 1000. Auxiliary system 300 evaluatesproblems P in main system 200. Auxiliary system 300 has service module310 to collect problem related data D from main system 200, acquisitionmodule 320 to acquire knowledge representations R, knowledge module 330to store knowledge representations R, inference module 340 forprocessing problem related data D with knowledge representations R toidentify solutions S and for forwarding the solutions S through servicemodule 310 to main system 200. Throughout the modules, auxiliary systemconsiders context and versions (of main system 200 and application A).

Auxiliary system 300 finds problem related data D by evaluating theproblem environment in main system 200: date, time, memory usage, dataobjects, software modules of application and operating system and thelike.

FIG. 2 illustrates main system 200 and auxiliary systems 300 with moredetail. Main system 200 has a client/server configuration with database210 (preferably, relational database), application server 220 andfront-end server 230.

The following refers to an exemplary implementation: Main system 200 andauxiliary system 300 are implemented by an R/3 type system. System 200performs an ERP application. The ERP application is defined byinstructions that have common keywords, common syntax and commonsemantic with environments selected from the group of: ABAB/4, Java 2Platform Enterprise Edition (J2EE), and .NET framework. Auxiliary system300 uses the client/server configuration (210, 220, 230) of main system200: the modules of auxiliary system 300 are distributed such thatservice module 310, acquisition module 320, knowledge module 330,inference module 340 are arranged in parallel to application server 220and to database 210. Front-end server 230 operates as user-interfaceboth for main system 200 and auxiliary system 300. In other words,database 210 implements a storing function, application server 220implements the application (A, cf. FIG. 1) and front-end server 230implements presentations (e.g., user interface). The moduledistributions can be modified. For example, knowledge module 330 can bepart of database 210. Internet communication is used between applicationserver 220 and front-end server 230. Internet communication isimplemented by well-known techniques (e.g., TCP/IP, HTML, and HTTP).

Having introduced main system 200 and auxiliary system 300 in general,the following sections describe the modules with more detail.

FIG. 3 illustrates service module 310, especially its cooperation withmain system 200 (dashed frame) in the exemplary implementation. Servicemodule 310 makes basis service functions of main system 200 availablefor auxiliary system 300 (cf. FIG. 2). Basis service functions are:ABAP/4 workbench, administration, authorizations, batch input, datadictionary, dialog control, framework, graphical user interface,application program interface, and job. Basis services are explained inthe above-cited reference books.

Service module 310 cooperates with main system 200 to obtain problemrelated data D for auxiliary system 300. Service module 310 cooperateswith database 210 to test the existence of objects: The problem relateddata D comprises information about existence and non-existence of theobjects. The objects are related to application A (in server 220).

Service module 310 cooperates with database 210 to obtain the content ofa table entry as problem related data D. Service module 310 recordsevents in the operating system of main system 200 by writing to database210. Service module 310 records problem related data D obtained fromdata consistency check operations of main system 200 (e.g., applicationserver 220). Consistency checks determine consistency (non-consistency)of data in database 210, especially in the database tables: Entries inthe table-body should be consistent with the entries in thetable-header. For example, the body holds zip-code numbers below headers“zip-code”. Service module 310 instructs front-end server 230 to providedialogs with user 1000. Service module 310 provides remote function call(RFC) connections with further auxiliary systems (with similar modules),such as with a service system (cf. FIG. 6).

Service module 310 monitors application server 220 and database 210according to instructions from inference module 340.

FIG. 4 illustrates acquisition module 320 for the exemplaryimplementation. Acquisition module 320 also modifies knowledgerepresentations R. Acquisition module 320 interacts with knowledgeengineer 1001, as illustrated, through tree 322 on a graphical userinterface. Acquisition module 320 uses tree 322 to represent theknowledge representations R as a semantic net. Engineer 1001 may usewell-known edit or drag and drop techniques to manipulate therepresentations.

Knowledge engineers are, for example, (a) software developers who arefamiliar with main system 200, (b) technical writers who writedocumentations for customers, and (c) persons that have gatheredexperience as being a user (cf. 1000 in FIG. 1). Tree 322 assistsengineer 1001 to modify knowledge representations. As in the example,tree 322 represents a rule relating to a patch type. The rule has aquery step (Download OK?) and conditional steps. A patch isdistinguished by its source between Compact Disk, OSS (online servicesystem, cf. reference books), and Internet. Depending on the source,different advices can be defined. Also, user 1001 is invited to modifytree 322 by inserting icons from an icon tray (“insert objects”).

FIG. 5 illustrates knowledge module 330 for the exemplaryimplementation. Knowledge module 330 stores knowledge representations Rby classifying into context, for example, business transactions as partof the application A, executable programs as part of the application A,and hierarchy level within the application A.

Optionally, knowledge module 330 uses lexicon 331 to distinguishversions of main system 200 (e.g., versions “1.0” and “2.0”). Lexicon331 defines parameters Pa (for a particular version) and knowledgerepresentations R (for all versions). The figure illustrates thedifferent parameters by different hatching. Distinguishing is veryconvenient if auxiliary system 300 serves 2 or more main systems 200that have different version (i.e. release or language). For example,main systems 200 might differ in their table definitions due todifferent release dates. Distinguishing through parameters for equalrepresentations helps to keep the number of knowledge representations ata convenient level. Useful is also to distinguish between differentnatural languages: For example, while a first main system communicateswith its users in German; a second main system communicates with itsusers in English; both mains systems are supported by a common auxiliarysystem that provides dialog texts in English or German. Knowledge module330 makes the knowledge representations R selectively available ornon-available according to a selected context or version.

Knowledge module 330 distinguishes context with primary context andsecondary context, wherein the secondary context is referenced from thefirst context. The first context can refer to the second context, thesecond context can refer to the first context, or both contexts canrefer to each other. Knowledge module 330 selects knowledgerepresentations R to be considered by inference module 340 according tothe context of a current transaction by the application server 220.Selected context is selected by user 1000 or by a predefined rule. Forexample, the context is selected from: system and program performance,background processing, OCS and patches, data dictionary, printerproblems, remote function calls and connectivity, R/3 reporting, andsecurity and administration.

For example, the first context is defined by the application with atransaction “Patch Manager”. Problem P and data D are “Patch not found”.Knowledge representations R have hints to find a storage location forpatches (e.g., a directory or a server). But processing does not resultin a solution S. The problem remains unsolved. However, the link“Transport” refers from the first context to the second context. Thesecond context leads to knowledge representations R to softwareinstalling procedures. Processing with these representations R leads tosolutions S.

Knowledge module 330 is adapted to receive regular updates of theknowledge representations R (arrow symbol). The updates can originate,for example, from a service system (cf. FIG. 5) that acts as furtherauxiliary system. Knowledge module 330 stores knowledge representationsR in database 210 with entries for specific problem P symptoms andcorresponding solutions S.

Knowledge module 330 stores knowledge representations R that point topredefined solution identification rules in database 210 (or in module330 itself). The solution identification rules are provided in a metalanguage. The meta-language is derived from ABAP/4.

The rules usually identify action, object, arguments and resultlocation. An example for meta language is, for example, a 2-line rulefor storing data. The first line reads as “CALL” (action), “FUNCTION”(object), “GET_FILE_PATH” (argument), and “FILE_PATH” (result location)to find a file directory (path) and to keep the file directory in afirst variable (“FILE PATH”). The second line reads as “CHECK_EXIST”(action), “FILE” (object), “<FILE_PATH>/rfc.trc” (argument) and “EXIST”(result location) to check the existence of file “rfc.trc” in thedirectory and to write existence/absence result into a variable(“EXIST”).

Providing R in meta-language in convenient for automatically processing.Markup languages (e.g., XML) are also useful. It is an advantage that Rcan also be provided partially or completely in natural languages (e.g.,English) for “processing” by a human.

Knowledge module 330 generates a structured set of problem solvingstrategies, such as so-called “troubleshooting guides”. These arestrategies for consideration by a specialist or by the user.

Knowledge module 330 generates solution identification rules withcomputer instructions that the computer utilizes to automatically solvethe problem. Preferably, knowledge module 330 distinguishes errorclasses (details below).

Sets of semantically related solution identification rules are groupedtogether, such as rules to find the problem “inconsistency in a table”,and rules to find the corresponding code to “automatically re-arrangethe table” (i.e. utilizing the solution). Tables in the database 210 arenot only used to organize data for application A, but also convenientfor knowledge module 330 to stores knowledge representations R. In thatcase, the tables are provided prior to activating auxiliary system 300.

The following is an example for using knowledge representations R,context and check lexicon: The knowledge representations form a set ofR1, R2, R3, . . . , R16, . . . , R99 (consecutively numbered). Each R isdefined by meta-language, such as “CHECK PRINTER CONNECTION” for R16.

Context are subsets of representations for that relate to problemclasses, such as context PRINTER with R5, R6 and R16. The check lexiconlists details for knowledge representations depending on versions ofmain system 200 (optionally versions of application A). R16 for version3.0 testing an IP connection by a standard “PING PRT” command to theprinter; for version 3.1 calling a dedicated test function (in auxiliarysystem 300); for version 4.0 instructing the printer to print a testpage.

When processing problem data D such as “printing not possible” for mainsystem 300 of version 3.0, auxiliary system 200 extract the context setPRINTER from all R, checks the lexicon for applicable details andapplies each R in combination with the details (e.g., R16 PING PRT). Ifa solution is still missing, the context changes, for example, toGENERAL with R1 “CHECK POWER SUPPLY”. Applying R1—this time withoutdistinguishing versions—leads to a success. The printer was notconnected to the mains power. The solution S is identified as a messageto the user that is forwarded to the user (in the further context of theEnglish language, through the front-end server): “Please connect yourprinter to the 230 volts power supply.”

FIG. 6 illustrates inference module 340 in auxiliary system 300 withmore detail. Inference module 340 identifies the solutions S from setsof predefined advices (preferably, advices of application A) by asolution identifier.

Inference module 340 identifies the solutions by applying the knowledgerepresentations R (to data D) in a predefined order that is, forexample, a sequential order (e.g., R1, R2, R3), a hierarchical order(e.g., R1, R21, R21, R31, R32), a dynamically adaptive order in that theorder might chance by conditional jumps or the like (e.g., IF THEN).

Inference module 340 communicates questions to user 1000 (cf. Q1, Q2, Q3reservoir). Preferably, the questions are standard questions. Inferencemodule 340 consecutively numbers the questions. Inference module 340composes the questions from predefined passages that are provided byapplication server 220. Inference module 340 analyzes the responses thatuser 1000 enters in natural language (cf. language analyzer).

So far the exemplary implementation has been described with main system200 and auxiliary system 300 that communicate by basis functions andthat are implemented by a single R/3 system. Distributing modules ofauxiliary system 300 to a client/server system configuration isconvenient. The invention is however not limited to that. Furtherimplementations benefit from the following:

-   -   (a) Main and auxiliary systems can be distributed to different        computer systems (e.g., different R/3 systems; cf. FIG. 7).    -   (b) A further auxiliary system (here called service system) can        provide enhanced problem evaluation capacities (cf. FIG. 7).    -   (c) One auxiliary system can serve 2 or more main systems (cf.        FIG. 8).    -   d) Applicable knowledge representations can be selected for a        particular version of the main system, for example, by        maintaining a check lexicon.    -   e) A first auxiliary system starts to evaluate the problem by        first knowledge representations and forwards evaluation results        to a second auxiliary system. The second auxiliary system then        returns a second (enhanced) knowledge representation to enable        the first auxiliary system to finish the evaluation.

For convenience of explanation, the following uses the term “system200/300” collectively for the combination of main system 200 withauxiliary system 300 for main system 200 alone. In other words,auxiliary system 300 is not longer required in any case.

FIG. 7 illustrates a first distributed system landscape with system200/300 coupled to a service system 500 via a network. Service system500 has auxiliary components with functions that are substantiallysimilar to that of auxiliary system 300: service module 510, acquisitionmodule 520, knowledge module 530, inference module 540 as well asmodules for front-end communication. The foregoing description isapplicable for these modules as well.

Preferably, system 500 operates independently from any main system anddoes not execute an ERP application. Optionally but not mandatory,service system 500 has a client/server configuration, such as system 500in an exemplary implementation being an R/3 type system. In comparisonto system 200, service system 500 uses knowledge representations R thatare enhanced in terms of volume, actuality, and complexity. If required,system 500 also solves problems in auxiliary system 300. In short,system 500 has at least the above-mentioned functions of auxiliarysystem 300 and serves as the expert system for system 200/300.

Service system 500 is conveniently installed at a manufacturer's site(of system 200/300) and communicates with system 200/300 by receivingproblem data (D) from system 200/300 and sending control instructions(C) to system 200/300.

Service system 500 is—optionally—operated by service engineer 1002 whohelps to solve problems in system 200/300. Dynamic enhancement ispossible: control instructions C are conveniently also used to regularlyupdate knowledge module 330 with actual knowledge representations (R,cf. FIG. 5 updates).

If system 200/300 is implemented with auxiliary system 300, thenauxiliary system 300 acts as a first expert system and service system500 act as a second expert system. Depending on the severity of theproblem (in system 200), problems are evaluated as follows:

-   -   (1) Auxiliary system 200 solves the problem (i.e. solutions S        are identified by system 300).    -   (2) Auxiliary system 200 does not solve the problem but forwards        a package with problem P data in combination with preliminary        solutions S (i.e., P/S data, based on knowledge        representations (R) in system 200) to service system 500.        Service system 500 then solves the problem.    -   (3) Auxiliary system 200 does not solve the problem but forwards        the P/S data package to service system 500. System 500 uses the        P/S data to return further knowledge representations. This        enables system 200 to evaluate and solve the problem.    -   (4) Service system 500 does not solve the problem automatically        and needs to interact with service engineer 1002 (further        analysis by a human technician).

FIG. 8 illustrates a second distributed system landscape with mainsystems 201 and 202 coupled to service system 500. The approach of FIG.7 has been expanded by adding a further main system. Optionally, servicesystem 500 is operated by a service engineer (cf. 1002).

In the exemplary implementation, main system 201 is physicallyimplemented on a first computer; main system 202 (as system 201 also inclient/server configuration) is implemented on a second computer,service system 500 is implemented on a third computer. Auxiliary systemsare optionally added to main systems 201 and 202.

Main system 201 is adapted to be operated by a first customer (e.g., afirst company), service system 500 is implemented by expertise serviceprovider ESP and main system 202 is adapted to be operated by a secondcustomer (e.g., a second company). For example, ESP is the manufacturerof systems 201/500/202 or is a consulting agency. Preferably, mainsystems 201 and 201 are systems of the same type (e.g., R/3), but havedifferent release versions (i.e. 201 older than 202, or vice versa).

Different release versions are distinguished by context groups. (cf.FIG. 5). Such an arrangement is convenient also for main systems thatcommunicate with their users in different natural languages. Whileproblem identification is technically the same in systems 201/202,messages to users (e.g., notes, dialogs) can be in different naturallanguages.

Some or all of the computers are located at physically differentlocations. Expertise of service system 500 becomes available around theglobe. Service system 500 could simultaneously serve main systems201/202 in different continents around the clock.

Having service system 500 physically separated from main systems 201/200has further beneficial effects: For example, expertise (i.e. knowledgerepresentations R) is shielded from access by main system 201/202; andsensitive data on main systems 201/202 is shielded from access byservice system 500.

Further distributions of main, auxiliary and service systems arepossible. For example, a plurality of main systems can be equipped withauxiliary systems that solve problems for their corresponding mainsystem or forward problem data to service systems.

FIG. 9 illustrates a simplified flowchart diagram of method 400 foroperating computer system 200/300. Method 400 is also applicable ifauxiliary system 300 is replaced by service system 500.

As stated above, system 200/300 has main system 200 executingapplication A in cooperation with human user 1000 and has auxiliarysystem 300 evaluating problems P in main system 200. According to method400, auxiliary system 300 performs the following steps: collecting 410problem related data D from main system 200, acquiring 420 knowledgerepresentations R, storing 430 knowledge representations R, processing441 problem related data D with knowledge representations R to identifysolutions S, and forwarding 442 the solutions S through service module310 to main system 200. Method steps are also illustrated in FIG. 1 byarrows COLLECT, ACQUIRE, STORE, PROCESS, FORWARD.

In the exemplary implementation, step collecting 410 is performed byservice module 310; step acquiring 420 is performed by acquisitionmodule 320; step storing 430 is performed by knowledge module 330; andsteps processing 441 and forwarding 442 are executed by inference module340. Modules 310-340 have been explained in connection with FIGS. 1-6.

In the exemplary implementation, steps collecting 410, acquiring 420,storing 430, processing 441 and forwarding 442 are performed for mainsystem 200 that has a client/server configuration with database 210,application server 220, and front-end server 230. Steps collecting 410,acquiring 420, storing 430, processing 441 and forwarding 442 areperformed in modules 310, 320, 330, 340 (of auxiliary system 300) thatare arranged in parallel to main system 200.

Steps acquiring 420 knowledge representations R and forwarding 442solutions S comprise to operate a user-interface in front-end server 230of main system 200. Steps collecting 410, acquiring 420, storing 430,processing 441 and forwarding 442 are performed by basis servicefunctions of main system 200.

The following is optional for step collecting 410: Service module 310cooperates with main system 200. Service module 310 cooperates withdatabase 210 to test the existence of objects: problem-related data Dinforms about existence and non-existence of the objects. Service module310 cooperates with database 210 to obtain the content of a table entryas problem related data D. Service module 310 records events in theoperating system of main system 200 by writing to database 210. Servicemodule 310 records problem related data D obtained from data consistencycheck operations of main system 300. Service module 310 instructsfront-end server 230 to provide dialogs with user 1000. Service module310 provides remote function call RFC connections with service system500 (operating like a remote auxiliary system). Service module 310monitors application server 220 and database 210 according toinstructions from inference module 340.

The following is optional for step acquiring 420: Acquisition module 320modifies the knowledge representations R. Acquisition module 320interacts with a knowledge engineer. Acquisition module 320 interactswith the knowledge engineer through tree 322 on a graphical userinterface (cf. FIG. 4). Acquisition module 320 uses tree 322 torepresent knowledge representations R as a semantic net.

The following is optional for step storing 430: Knowledge module 330classifies the knowledge representations R into context groups.Knowledge module 330 organizes the context groups by lexicon 331 (cf.FIG. 5). Knowledge module 330 defines the context by a version of mainsystem 200 and defines lexicon 331 by knowledge representations for theversions. Knowledge module 330 makes the knowledge representations Rselectively available or non-available according to a selected contextfor subsequent step processing 441. Knowledge module 330 distinguishescontext between primary context and secondary context. Knowledge module330 stores knowledge representations R in database 210 with entries forspecific problem P symptoms and corresponding solutions S. Knowledgemodule 330 stores knowledge representations R in database 210 withentries for predefined solutions identification rules. Knowledge module330 stores knowledge representations R in a plurality of tables indatabase 210.

The following is optional for step processing 441: Inference module 340performs an action such as to: identify the solutions S form a set ofpredefined advices of the application A, identify the solutions S byapplying knowledge representations R in a sequential order, identify thesolutions S by applying knowledge representations R in a hierarchicalorder, identify the solutions S by applying knowledge representations Rin a dynamically adaptive order, communicate questions to user 1000 bycomposing the questions from predefined passages provided by applicationA, analyse responses that user 1000 enters in natural language.

Optionally, systems 200/300 cooperate with service system 500 (cf. FIG.7): While executing any of steps collecting 410, acquiring 420, storing430, processing 441 and forwarding 442, auxiliary system 300conditionally forwards problem P data in combination with solutions S toservice system 500. In the alternative, auxiliary system 300 forwardsproblem P data and solutions S for further analysis by a humantechnician. Auxiliary system 300 forwards problem P data and solutions Sin a format that allows analysis by an expert system, for example byservice system 500.

In short, executing method 400 depends on a variety of circumstances.FIGS. 9-10 give exemplary overviews for scenarios that developdynamically and that adapt the particular circumstances.

For illustration, exemplary scenarios refer to interaction (FIG. 10),context (FIG. 11) and distribution of problem collecting and solutionprocessing (FIG. 12). The following description denotes queries byquestion marks.

FIG. 10 illustrates a simplified scenario that considers interaction,and thereby distinguishes to automatically evaluate the problem and tosemi-automatically evaluate the problem. Interaction occurs amongsystems 200, 300 and 500 and human user 1000.

-   -   (10) Is interaction between main system 200 and auxiliary system        300 (or service system 500) required? The yes/no answer could        depend on the performance during collecting or processing steps.    -   (30) If no, system 200 continues to automatically evaluate the        problem or continues with normal operation, usually in the        absence of any problems.    -   (20) If yes (interaction required): What is the interaction        type: user/system (U/S) interaction (e.g., 200, 300 or 500 with        1000) or system/system (S/S) interaction (e.g., 200 with 300,        200 with 500, 300 with 500)?    -   (21) If user/system interaction, is the interaction initiated by        a user or initiated by a system?    -   (22) If initiated by a user, for example, user 1000 detects a        problem and starts a voluntary dialog with auxiliary system 300        or with system 500 at any time. A good opportunity for starting        is to press a specialized button (“PROBLEM ANALYSIS”) when        viewing an error message.    -   (23) If initiated by a system, for example, system 300 collects        problem related data (D) by providing a mandatory dialog (system        300 with user 1000).    -   (24) If system/system interaction, is the interaction initiated        by a user or initiated by a system?    -   (25) If initiated by a user, for example, user 1000 starts the        operation of 200/300 (cf. description method 400) or the        operation of 200/500.    -   (26) If initiated by a system, for example, system 200/500        starts its operation automatically.

The query order can be modified: the query for U/S or S/S interaction(10) could follow the initializing queries. U/S and S/S interactions andinitializations can be related to each other.

FIG. 11 illustrates a simplified scenario that considers primary andsecondary context.

-   -   (10) Processing problem data D to identify context (i.e. first        context) and versions, thereby using lexicon 331 (cf. FIG. 5).    -   (20) Selecting knowledge representations R for that context (or        version).    -   (30) Processing problem data D and knowledge representations R        to find solutions S.    -   (40) Querying for the existence of a solution and finishing if        solution exists.    -   (50) Repeating processing to identify further context (i.e.        second or higher context, or other versions) and querying until        a solution S is identified until all contexts have been        considered.

FIG. 12 illustrates a simplified scenario that considers thedistribution of problem collecting and solution processing for thevarious systems. The scenario is useful for distributed systems withmain system 200, auxiliary system 300, and service system 500 (cf. FIGS.7-8). Depending on the availability of knowledge representations R (inauxiliary system 200 and in service system 500) that match to problemdata D, the problem is automatically evaluated and solution S isidentified by auxiliary system 200 or service system 500, or the problemis manually evaluated and the solution S is found by a human (e.g., useror technician). Modifications to the scenario (semi-automaticallyevaluating and solving) are also possible.

The scenario substantially has the following phases:

-   -   (10) Detecting the problem in main system 200.    -   (20) Processing data D with R by auxiliary system 300 to        identify a solution S (cf. method 400).    -   (30) If processing successes to a solution S, solving the        problem by auxiliary system 300.    -   (40) If processing fails (no solution), forwarding data D to        service system 500 (optionally enhancing D as described above).    -   (50) Processing data D with R by service system 500 to identify        a solution S (applying method 400 analogously).    -   (60) If processing successes to a solution S, utilizing S (i.e.        solve the problem) by service system 500 (or by auxiliary system        300 that is instructed by service system 500).    -   (65) Optionally, supplying new R to auxiliary system (for        finding a final solution by the auxiliary system).    -   (70) If processing fails, evaluating the problem and finding S        by a human.

Once a solution S has been identified, it can be applied to actuallysolve the problem in a similar scenario.

It is within the scope of the invention to superimpose such and otherscenarios, for example, to have a scenario with interaction queries,with context consideration and with distribution.

FIG. 13 illustrates simplified exemplary scenarios for solving problemsin main system 200. Phases are given in top-down direction. Phases onthe left side belong to the first exemplary scenario with automaticallysolving by the auxiliary system (illustrated on the left); phases on theright side belong to semi-automatically solving by forwarding enhanceddata to service system 500. Processing phases are similar and thereforeillustrated for both sides.

As illustrated for the first scenario, main system 200 reports a problem(i.e. problem P); auxiliary system 300 collects the data (cf. step 410)to analyze the problem environment (i.e., operating system; versions;tables); system 300 enhances the data by the problem environment; system300 processes data (D) and knowledge representations (R) (problemanalysis) to identify the solution (S). (cf. FIG. 9, (10)(30))

As illustrated for the second scenario, user 1000 initiates problemdiagnosis, for example, main system 200 reports a problem (i.e. P).Auxiliary system 300 processes data (D) and knowledge representations(R), but does not identify a solution (e.g., R insufficient, D unknown).The user (or a computer specialist) manually collects further problemrelated data (e.g., about operating system); starts processing again toenhance the problem data (D) by environment data; re-processing bysystem 300 does however not result in a solution (S). Therefore, theuser decides to forward the enhanced problem data to service system 300.Service system then starts to re-evaluate the problem, usually with amore comprehensive set of knowledge representations (R) and solutions(S). (cf. FIG. 9, (10), (20), (21), (22), (10), (20), (24), (26))

FIGS. 9-13 concentrate on examples. Further scenarios can beimplemented, using other yes/no queries or case distinctions. Examplesare explained in the following.

-   -   (a) Is there a need to manually input problem data D? For some        cases, especially for rare problems, preparing comprehensive        knowledge representations R is not possible (or too expensive).        The user can enter data via dialogs or other user interface        elements.    -   (b) Does knowledge module 330 has a sufficient number of        knowledge representations R? The number is usually sufficient if        processing (step 441) results in solutions S. The number is        usually insufficient if processing does not result in        solutions S. In that case, activating acquisition module 320 and        knowledge module 330 is possible to add or modify knowledge        representations R.    -   (c) Does knowledge module 330 need to obtain further knowledge        representations R? This query is related to the previous one.        Representations (R) can be obtained from distributed systems.        For example, system 300 can obtain R from system 500. This is        convenient, for example, if system 300 operates at a customer        site (occasional update) and system 500 operates at the site of        a system manufacturer (daily update).    -   (d) Are there updates available for modules 310, 320, 330, 340,        for knowledge representations (R) or the like? Again, updates        can be loaded from system 500 to system 300.    -   (e) Is human support service available in a daylight time-zone        to solve a problem in a night-light time-zone? If service        systems 500 and specialists (i.e. service engineer 1002) are        required, then system 200/300 could send a request to system 500        in a daylight time-zone. A 24-hour-service can be established        with specialists working during daylight hours.    -   (f) Is there an emergency in solving the problem or is there        allowable waiting time? Are there 2 or more problems with        different urgency or priority levels? Prioritizing adds value;        solutions to high-profile problems could be searched in parallel        by system 300 and by system 500.    -   (g) Did the problem cause immediate symptoms? Some problems show        symptoms only if they have become severe (e.g., a table with        data overflow). Auxiliary system 300 can evaluate the        performance of the main system to find problems before they        appear to the user. Conveniently, systems 300 or 500 operate in        the background to identify hidden problems and operate in the        foreground (i.e. with interaction) to solve visible problems.    -   (h) Having the solution identified, is there a predefined        instructions sequence assigned to that solutions to        automatically solve the problem? Automatic (or semi-automatic)        problem solving according to predefined instructions could        follow processing.    -   (i) It the problem classified in a particular error class?        Problems and their corresponding solutions can be classified in        great variety. Exemplary classes are:    -   Class “information”, auxiliary system 300 informs user 1000        about application details that are usually not critically to        main system 200;    -   Class “operation error”, user 1000 does not properly operate        system 200 (e.g., by accident), system 200 does not behave as        specified, such problems can be solved, for example, by        informing user 1000 through messages.    -   Class “performance”, system 200 exhibits relatively long        response times; problems like this are often related to hardware        failure or to overflow of tables.    -   Class “wrong result”, system 200 operates stable, but results        are calculated incorrectly or inconsistently, exemplary        solutions are consistency correcting or debugging.    -   Class “system error”, system 200 detects an invalid processing        step; an exemplary solution is the identification of the system        module that causes the error (tracking function).    -   Class “cancellation”, system 200 partly or completely stops to        operate, an exemplary solution is to reproduce the error.    -   (j) Can evaluating (i.e. method 400) be repeated with modified        parameters (i.e. iteration with different D, R, or S)?

Without departing from the scope of the invention, persons of skill inthe art may add further functionality, such as for context retrieval,long-text messages, heuristics, artificial intelligence, or exceptionhandling.

FIG. 14 summarizes various aspects of the present invention byconcentrating on inference module 340/540 (collectively x40). Asexplained above, inference module x40 processes problem related data Dwith knowledge representations R to identify solutions S.

Modules that make inference module x40 an integral part of an expertsystem (i.e. provide expertise functionality) have been explained above:modules for obtaining D and R (e.g., collect, acquire, store), to use Sand—optionally—to interact with a human user.

Briefly, inference module x40 has expertise functionality for evaluatingproblems P in main computer system 200 that executes an application A.Inference module x40 is adapted to process problem related data D withknowledge representations R to identify solutions S. For example,inference module x40 has one of the following configurations:

In a first configuration, inference module x40 is part of auxiliarycomputer system 300 that uses basis functions of main computer system200. Main computer system 200 and auxiliary computer system 300 areclient/server systems.

In a second configuration, inference module x40 is part of servicesystem 500 that receives problem related data D from main computersystem 200 over a network and that returns solutions S to main computersystem 200. In a first case, service system 500 returns solutions S thatsolve the problem directly. In a second case, service system 500 returnsolutions S that solve the problem indirectly by being further knowledgerepresentations for a further inference module (e.g., module 340).

In a third configuration, inference module x40 distinguishes problemrelated data D and—optionally—knowledge representations R in contextclasses.

In a fourth configuration, inference module x40 is part of servicesystem 500 that receives problem related data D from first and secondmain systems 201, 202 of different versions over a network. Inferencemodule x40 applies knowledge representations R for both main systems201, 202 and distinguishes version differences of the main systems bylooking up in check lexicon 331.

The present invention can also summarized as follows:

Preferably, selected context is selected by user 1000. Preferably,selected context is selected by a predefined rule. Preferably, knowledgemodule 330 applies a predefined rule to select knowledge representationsR to be considered by inference module 340 according to the context of acurrent transaction in application server 220. Preferably, context isselected from the group of: system and program performance, backgroundprocessing, OCS and patches, data dictionary, printer problems, remotefunction calls and connectivity, R/3 reporting, and security andadministration. Preferably, main system 200 has a client/serverconfiguration with database 210, application server 220 and front-endserver 230. Preferably, computer system 200/300 is a system of an R/3type.

Preferably, main system 200 executes application A as an enterpriseresource planning ERP application. Preferably, the EPR application isselected from the group of: supply chain management, customerrelationship management, financials, human resources, enterpriseportals, exchanges, technology, product lifecycle management, supplierrelationship management, business intelligence, business intelligence,mobile business, hosted solutions, small and midsize business, industrysolutions. Preferably, the ERP application is defined by instructionsthat have common keywords, common syntax and common semantic withenvironments selected from the group of: ABAB/4, Java 2 PlatformEnterprise Edition J2EE, and .NET framework. Preferably, auxiliarysystem 300 uses client/server configuration 210, 220, 230 of main system200; the modules of auxiliary system 300 are distributed such thatservice module 310, acquisition module 320, knowledge module 330, andinference module 340 are arranged in parallel to application server 220and to database 210. Preferably, front-end server 230 operates asuser-interface for the main system 200 and for auxiliary system 300.Preferably, computer system 200/300 uses Internet communication betweenapplication server 220 and front-end server 230.

Preferably, service module 310 makes basis service functions of mainsystem 200 available for auxiliary system 300. Preferably, the basisservice functions are selected from the group of ABAP/4 workbench,administration, authorizations, batch input, data dictionary, dialogcontrol, framework, graphical user interface, application programinterface, and job. Preferably, service module 310 cooperates with mainsystem 200 to obtain problem related data D for auxiliary system 300.Preferably, service module 310 cooperates with database 210 to test theexistence of objects, wherein problem related data D comprisesinformation about existence and non-existence of the objects.Preferably, service module 310 cooperates with database 210 to obtainthe content of a table entry as problem related data D. Preferably,service module 310 records events in the operating system of main system200 by writing to database 210. Preferably, service module 310 recordsproblem related data D obtained from data consistency check operationsof main system 200. Preferably, service module 310 instructs front-endserver 230 to provide dialogs with user 1000.

Preferably, service module 310 provides remote function call RFCconnections with service system 500. Preferably, service module 310monitors application server 220 and database 210 according toinstructions from inference module 340.

Preferably, acquisition module 320 also modifies knowledgerepresentations R. Preferably, acquisition module 320 interacts withknowledge engineer 1001. Preferably, acquisition module 320 interactswith knowledge engineer 1000 through tree 322 on a graphical userinterface. Preferably, acquisition module 320 uses tree 322 to representknowledge representations R as a semantic net.

Preferably, knowledge module 330 is adapted to receive regular updatesof the knowledge representations R from service system 500. Preferably,knowledge module 330 stores the knowledge representations R in database210 with entries for specific problem P symptoms and correspondingsolutions S. Preferably, knowledge module 330 stores knowledgerepresentations R in the database 210 with entries for predefinedsolution identification rules. Preferably, the solution identificationrules are provided in a meta language. Preferably, the meta-language isderived from ABAP/4.

Preferably, knowledge module 330 generates a structured set of problemsolving strategies. Preferably, knowledge module 330 generates solutionidentification rules with computer instructions to automatically solvethe problem. Preferably, computer system 200/300 is adapted to use thesolution identification rules for automatically solving the problem.Preferably, sets of semantically related solution identification rulesare grouped together. Preferably, knowledge module 330 stores knowledgerepresentations R in a plurality of tables in database 210. Preferably,the tables are provided prior to activating auxiliary system 300.

Preferably, inference module 340 identifies the solutions S from sets ofpredefined advices. Preferably, inference module 340 identifies thesolutions S from sets of predefined advices that are advices of theapplication A. Preferably, inference module 340 identifies the solutionsby applying the knowledge representations R. Preferably, inferencemodule 340 applies the knowledge representations R in a predefinedorder. Preferably, the predefined order is a sequential order.Preferably, the predefined order is a hierarchical order. Preferably,the predefined order is a dynamically adaptive order. Preferably,inference module 340 communicates questions to user 1000 via front-endserver 230. Preferably, inference module 340 communicates questions thatare standard questions. Preferably, inference module 340 consecutivelynumbers the questions. Preferably, inference module 340 composes thequestions from predefined passages that are provided by the applicationserver 220. Preferably, inference module 340 analyzes responses that theuser enters in natural language. Preferably, auxiliary system 300conditionally forwards problem P data to service system 500.

Preferably, auxiliary system 300 forwards the problem P data to servicesystem 500 with preliminary analysis data based on processing withknowledge representations R in auxiliary system 200. Preferably,auxiliary system 300 forwards problem P data for further analysis by ahuman technician.

Preferably, auxiliary system 300 forwards problem data P and preliminarysolutions S to service system 500 in a format that allows evaluation inthe service system 500. Preferably, main system 201 is physicallyimplemented by a first computer, service system 500 is implemented by asecond computer and further main system 202 is implemented by a thirdcomputer. Preferably, main system 201 is adapted to be operated by afirst customer, service system 500 is implemented by an expertiseservice provider ESP, and the at least one further main system 201 isadapted to be operated by a second customer.

Preferably, main system 200 and the further main system 201 are systemsof the same type, but have different release versions. Preferably, someof the computers are located at physically different locations.Preferably, computer system 200/300 has a further module to identify apredefined instructions sequence, wherein the instruction sequence isassigned to solution S that is identified by inference module 340.

FIG. 15 illustrates a simplified block diagram of exemplary computersystem 999 in general for that the present invention can be implemented.System 999 has a plurality of computers 900, 901, 902 (or even more).Computer 900 can communicate with computers 901 and 902 over network990. Computer 900 has processor 910, memory 920, bus 930, and,optionally, input device 940 and output device 950 (I/O devices, userinterface 960). As illustrated, the invention is implemented by computerprogram product 100 (CPP), carrier 970 and signal 980.

In respect to computer 900, computer 901/902 is sometimes referred to as“remote computer”, computer 901/902 is, for example, a server, a peerdevice or other common network node, and typically has many or all ofthe elements described relative to computer 900.

Computer 900 is, for example, a conventional personal computer. (PC), adesktop device or a hand-held device, a multiprocessor computer, a pencomputer, a microprocessor-based or programmable consumer electronicsdevice, a minicomputer, a mainframe computer, a personal mobilecomputing device, a mobile phone, a portable or stationary personalcomputer, a palmtop computer or the like.

Processor 910 is, for example, a central processing unit (CPU), amicro-controller unit (MCU), digital signal processor (DSP), or thelike.

Memory 920 is elements that temporarily or permanently store data andinstructions. Although memory 920 is illustrated as part of computer900, memory can also be implemented in network 990, in computers 901/902and in processor 910 itself (e.g., cache, register), or elsewhere.Memory 920 can be a read only memory (ROM), a random access memory(RAM), or a memory with other access options. Memory 920 is physicallyimplemented by computer-readable media, for example: (a) magnetic media,like a hard disk, a floppy disk, or other magnetic disk, a tape, acassette tape; (b) optical media, like optical disk (CD-ROM, digitalversatile disk—DVD); (c) semiconductor media, like DRAM, SRAM, EPROM,EEPROM, memory stick.

Optionally, memory 920 is distributed. Portions of memory 920 can beremovable or non-removable. For reading from media and for writing inmedia, computer 900 uses well-known devices, for example, disk drives,or tape drives.

Memory 920 stores modules such as, for example, a basic input outputsystem (BIOS), an operating system (OS), a program library, a compiler,an interpreter, and a text-processing tool. Modules are commerciallyavailable and can be installed on computer 900. For simplicity, thesemodules are not illustrated.

CPP 100 has program instructions and—optionally—data that causeprocessor 910 to execute method steps of the present invention. In otherwords, CPP 100 can control the operation of computer 900 and itsinteraction in network system 999 so that is operates to perform inaccordance with the invention. For example and without the intention tobe limiting, CPP 100 can be available as source code in any programminglanguage, and as object code (“binary code”) in a compiled form.

Although CPP 100 is illustrated as being stored in memory 920, CPP 100can be located elsewhere. CPP 100 can also be embodied in carrier 970.

Carrier 970 is illustrated outside computer 900. For communicating CPP100 to computer 900, carrier 970 is conveniently inserted into inputdevice 940. Carrier 970 is implemented as any computer readable medium,such as a medium largely explained above (cf. memory 920). Generally,carrier 970 is an article of manufacture having a computer readablemedium with computer readable program code to cause the computer toperform methods of the present invention. Further, signal 980 can alsoembody computer program product 100.

Having described CPP 100, carrier 970, and signal 980 in connection withcomputer 900 is convenient. Optionally, further carriers and furthersignals embody computer program products (CPP) to be executed by furtherprocessors in computers 901 and 902.

Input device 940 provides data and instructions for processing bycomputer 900. Device 940 can be a keyboard, a pointing device (e.g.,mouse, trackball, cursor direction keys), microphone, joystick, gamepad, scanner, or disc drive. Although the examples are devices withhuman interaction, device 940 can also be a device without humaninteraction, for example, a wireless receiver (e.g., with satellite dishor terrestrial antenna), a sensor (e.g., a thermometer), a counter(e.g., a goods counter in a factory). Input device 940 can serve to readcarrier 970.

Output device 950 presents instructions and data that have beenprocessed. For example, this can be a monitor or a display, (cathode raytube (CRT), flat panel display, liquid crystal display (LCD), speaker,printer, plotter, vibration alert device. Output device 950 cancommunicate with the user, but it can also communicate with furthercomputers.

Input device 940 and output device 950 can be combined to a singledevice. Any device 940 and 950 can be provided optional.

Bus 930 and network 990 provide logical and physical connections byconveying instruction and data signals. While connections insidecomputer 900 are conveniently referred to as “bus 930”, connectionsbetween computers 900-902 are referred to as “network 990”. Optionally,network 990 includes gateways which are computers that specialize indata transmission and protocol conversion.

Devices 940 and 950 are coupled to computer 900 by bus 930 (asillustrated) or by network 990 (optional). While the signals insidecomputer 900 are mostly electrical signals, the signals in network areelectrical, electromagnetic, optical or wireless (radio) signals.

Networks are commonplace in offices, enterprise-wide computer networks,intranets and the Internet (e.g., world wide web). Network 990 can be awired or a wireless network. To name a few network implementations,network 990 can be, for example, a local area network (LAN), a wide areanetwork (WAN), a public switched telephone network (PSTN); a IntegratedServices Digital Network (ISDN), an infra-red (IR) link, a radio link,like Universal Mobile Telecommunications System (UMTS), Global Systemfor Mobile Communication (GSM), Code Division Multiple Access (CDMA), orsatellite link.

A variety of transmission protocols, data formats and conventions isknown, for example, as transmission control protocol/internet protocol(TCP/IP), hypertext transfer protocol (HTTP), secure HTTP, wirelessapplication protocol (WAP), unique resource locator (URL), a uniqueresource identifier (URI), hypertext markup language (HTML), extensiblemarkup language (XML), extensible hypertext markup language (XHTML),wireless markup language (WML), Standard Generalized Markup Language(SGML).

Interfaces coupled between the elements are also well known in the art.For simplicity, interfaces are not illustrated. An interface can be, forexample, a serial port interface, a parallel port interface, a gameport, a universal serial bus (USB) interface, an internal or externalmodem, a video adapter, or a sound card.

Computer and program are closely related. As used hereinafter, phrases,such as “the computer provides” and “the program provides”, areconvenient abbreviation to express actions by a computer that iscontrolled by a program.

REFERENCES

-   1000 user-   1001 knowledge engineer-   1002 service engineer-   200 main system-   200/300 computer system-   210, 201 first, second main systems-   210 relational database-   220 application server-   230 front-end server-   300 auxiliary system-   310 service module-   320 acquisition module-   322 tree-   330 knowledge module-   331 lexicon-   340 inference module-   500 service system-   500 service system-   510 service module-   520 acquisition module-   530 knowledge module-   540 inference module-   A application-   C control instructions-   D data-   P problem-   Pa parameter-   R knowledge representations-   S solution-   S/S system/system-   U/S user/system-   XX queries; query results; X=0 . . . 9

1. A computer system comprising: a main system that executes anapplication in cooperation with a human user; an auxiliary system toevaluate problems in the main system using a service module to collectproblem related data from the main system, wherein the auxiliary systemdetermines a context of the evaluated problems and distinguishesversions of the main system; a knowledge module that stores knowledgerepresentations by classifying the knowledge representations intocontext groups, wherein, each context group is classified according toat least one predefined context, the knowledge representations compriseentries for specific problem symptoms and corresponding solutions, theknowledge module distinguishes the context with a primary context and asecondary context, with the secondary context referenced from theprimary context, and the knowledge module makes knowledgerepresentations selectively available or non-available according to aselected context; an inference module that processes problem relateddata with knowledge representations where the context of the evaluatedproblems is used to select at least one context group of the knowledgerepresentations to identify solutions, wherein the inference moduleforwards the solutions through the service module to the main system. 2.The computer system of claim 1, wherein the auxiliary systemdistinguishes context and versions relating to the application.
 3. Thecomputer system of claim 2, wherein the auxiliary system distinguishescontext and versions by using a check lexicon in the knowledge module.4. The computer system of claim 3, wherein the check lexicon listsdetails for the knowledge representations, wherein the details depend ona version of the main system.
 5. The computer system of claim 3, whereinthe check lexicon lists details for the knowledge representations,wherein the details depend on a version of the application.
 6. Thecomputer system of claim 3, wherein the check lexicon lists details forthe knowledge representations, wherein the details depend on the contextof the problem.
 7. The computer system of claim 3, wherein the checklexicon lists details for the knowledge representations that depend on aversion of the main system.
 8. The computer system of claim 3, whereinthe check lexicon uses parameters for versions and context.
 9. Acomputer system comprising: a main system that executes an applicationin cooperation with a human user; an auxiliary system to evaluateproblems in the main system using a service module to collect problemrelated data from the main system, wherein the auxiliary systemdetermines a context of the evaluated problems and distinguishesversions of the main system; a knowledge module that stores knowledgerepresentations by classifying the knowledge representations intocontext groups, wherein each context group is classified according to atleast one predefined context, wherein the knowledge representationscomprise entries for specific problem symptoms and correspondingsolutions, and wherein the knowledge module makes knowledgerepresentations selectively available or non-available according to aselected context; and an inference module that processes problem relateddata with knowledge representations where the context of the evaluatedproblems is used to select at least one context group of the knowledgerepresentations to identify solutions, wherein the inference moduleforwards the solutions through the service module to the main system.10. The computer system of claim 9, wherein the auxiliary systemdistinguishes context and versions relating to the application.
 11. Thecomputer system of claim 10, wherein the auxiliary system distinguishescontext and versions by using a check lexicon in the knowledge module.12. The computer system of claim 11, wherein the check lexicon listsdetails for the knowledge representations, wherein the details depend ona version of the main system.
 13. The computer system of claim 11,wherein the check lexicon lists details for the knowledgerepresentations, wherein the details depend on a version of theapplication.
 14. The computer system of claim 11, wherein the checklexicon lists details for the knowledge representations, wherein thedetails depend on the context of the problem.
 15. The computer system ofclaim 11, wherein the check lexicon lists details for the knowledgerepresentations that depend on a version of the main system.
 16. Thecomputer system of claim 11, wherein the check lexicon uses parametersfor versions and context.